2010Unpublished venueRequires access

SMATO: Simultaneous mask and target optimization for improving lithographic process window

Shayak Banerjee, Kanak B. Agarwal, Michael Orshansky

Open publisher page 12 citations

Abstract

Low-k1 lithography results in features that suffer from poor lithographic yield in the presence of process variation. The problem is especially pronounced for lower level metals used for local routing, where bi-directionality gives rise to lithography unfriendly layout patterns. However, one can modify such wires without significantly affecting design behavior. In this paper, we propose to simultaneously modify mask and target during OPC to improve lithographic yield. The method uses image slope information, available during image simulation at no extra cost, as a measure of process window. We derive a cost function that maximizes both contour fidelity and robustness to drive our simultaneous mask and target optimization (SMATO) method. We then develop analytical equations to predict the cost for a given mask and target modification and use a fast algorithm to minimize this cost function to obtain an optimal mask and target solution. Our experiments on sample metal1 (M1) layouts show that the use of SMATO reduces the Process Manufacturability Index (PMI) by 15.4% compared to OPC, which further leads to 69% reduction in the number of layout hotspots. Additionally, such improvement is obtained at low average runtime overhead (5.5%). Compared to PWOPC, we observe 4.6% improvement in PMI at large (2.6X) improvement in runtime.

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What this paper is about

Low-k1 lithography results in features that suffer from poor lithographic yield in the presence of process variation. The problem is especially pronounced for lower level metals used for local routing, where bi-directionality gives rise to lithography unfriendly layout patterns. However, one can modify such wires without significantly affecting design behavior. In this paper, we propose to simultaneously modify mask and target during OPC to improve lithographic yield. The method uses image slope information, available during image simulation at no extra cost, as a measure of process window. We derive a cost function that maximizes both contour fidelity and robustness to drive our simultaneous mask and target optimization (SMATO) method. We then develop analytical equations to predict the cost for a given mask and target modification and use a fast algorithm to minimize this cost function to obtain an optimal mask and target solution. Our experiments on sample metal1 (M1) layouts show that the use of SMATO reduces the Process Manufacturability Index (PMI) by 15.4% compared to OPC, which further leads to 69% reduction in the number of layout hotspots. Additionally, such improvement is obtained at low average runtime overhead (5.5%). Compared to PWOPC, we observe 4.6% improvement in PMI at large (2.6X) improvement in runtime.

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Available abstract

Low-k1 lithography results in features that suffer from poor lithographic yield in the presence of process variation. The problem is especially pronounced for lower level metals used for local routing, where bi-directionality gives rise to lithography unfriendly layout patterns. However, one can modify such wires without significantly affecting design behavior. In this paper, we propose to simultaneously modify mask and target during OPC to improve lithographic yield. The method uses image slope information, available during image simulation at no extra cost, as a measure of process window. We derive a cost function that maximizes both contour fidelity and robustness to drive our simultaneous mask and target optimization (SMATO) method. We then develop analytical equations to predict the cost for a given mask and target modification and use a fast algorithm to minimize this cost function to obtain an optimal mask and target solution. Our experiments on sample metal1 (M1) layouts show that the use of SMATO reduces the Process Manufacturability Index (PMI) by 15.4% compared to OPC, which further leads to 69% reduction in the number of layout hotspots. Additionally, such improvement is obtained at low average runtime overhead (5.5%). Compared to PWOPC, we observe 4.6% improvement in PMI at large (2.6X) improvement in runtime.

Key concepts: Lithography, Design for manufacturability, Process window, Optical proximity correction, Robustness (evolution), Computer science, Critical dimension, Process variation

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